{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/faster-neural-networks-straight-from-jpeg","title":"Faster Neural Networks Straight from JPEG","arxiv_id":null,"date":"2018-12-01","proceeding":"NeurIPS 2018 12","authors":["Lionel Gueguen","Alex Sergeev","Ben Kadlec","Rosanne Liu","Jason Yosinski"],"abstract":"The simple, elegant approach of training convolutional neural\n  networks (CNNs) directly from RGB pixels has enjoyed overwhelming\n  empirical success. But can more performance be squeezed out of\n  networks by using different input representations?  In this paper we\n  propose and explore a simple idea: train CNNs directly on the\n  blockwise discrete cosine transform (DCT) coefficients computed and\n  available in the middle of the JPEG codec. Intuitively, when\n  processing JPEG images using CNNs, it seems unnecessary to\n  decompress a blockwise frequency representation to an expanded pixel\n  representation, shuffle it from CPU to GPU, and then process it with\n  a CNN that will learn something similar to a transform back to\n  frequency representation in its first layers. Why not skip both\n  steps and feed the frequency domain into the network directly?  In\n  this paper we modify \\libjpeg to produce DCT coefficients directly,\n  modify a ResNet-50 network to accommodate the differently sized and\n  strided input, and evaluate performance on ImageNet. We find\n  networks that are both faster and more accurate, as well as networks\n  with about the same accuracy but 1.77x faster than ResNet-50.","url_abs":"http://papers.nips.cc/paper/7649-faster-neural-networks-straight-from-jpeg","url_pdf":"http://papers.nips.cc/paper/7649-faster-neural-networks-straight-from-jpeg.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"faster-neural-networks-straight-from-jpeg","repo_url":"https://github.com/uber-research/jpeg2dct","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"discrete-cosine-transform","method_name":"Discrete Cosine Transform"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}